Improving Parameterization of Ice Microphysical Processes in Arctic Clouds Using a Synergistic Modeling and Observational Approach

Abstract

Ice microphysical processes play a critical role in governing the life cycles, structures, and radiative properties of cold clouds across our planet, but our understanding of ice processes remains relatively poor. As a result, significant uncertainties exist in parameterizations of ice particle evolution in atmospheric models of all scales. Recent modeling work attempts to evolve ice particle mass, shape, size, density and fall speed through a more direct coupling with laboratory-measured and observed quantities. Thus far, however, these “particle property” models and radar observations have not been combined quantitatively – let alone with full consideration of the uncertainties inherent in each – to yield insights into ice processes. Such an effort is uniquely capable of providing better understanding and representation of cold-cloud processes.
Our main objectives are to:

• Improve the fundamental quantitative models of ice growth processes using ARM radar observations;
• Better understand the growth of ice particles in Arctic clouds using a novel particle property model developed under our previous ASR-funded project; and
• Robustly estimate and constrain key unknown model parameters using ARM radar observations combined with the particle property model to develop an improved parameterization of ice growth processes for traditional bulk parameterizations used in cloud and climate models.

We will accomplish these objectives by:

• Using laboratory data to constrain basic ice growth parameters;
• Using the particle property model in conjunction with ARM polarimetric and Doppler radar observations to perform parameter estimation techniques in a Bayesian statistical framework to derive estimates of key model microphysics parameters from simulations of layered Arctic cloud systems and to quantify the uncertainty associated with these parameters;
• Using the synergy of particle property modeling, radar observations, and parameter estimation to derive improved coefficients for power-law relationships used in traditional microphysics schemes, thus allowing such schemes to emulate the behavior of particle property models; and
• Using combined modeling and observational techniques to better understand microphysical and dynamical processes operating in layered Arctic clouds, and exploring the relative importance of mesoscale and microscale processes in controlling cloud structures and lifetimes.

Our research will result in an improved understanding of fundamental ice growth processes. The synergy of ice particle property modeling, advanced radar observations, and robust parameter estimation will directly lead to determination of environmental controls on growth processes, identification of key uncertainties to help guide future research, and observationally constrained mass-dimensional relationships that may be incorporated into any ice modeling system. A major advantage of this approach is that most existing model parameterizations already include such a framework for treating ice processes, so direct implementation of our results for improved simulation of cloud systems is highly feasible.